Hyperspectral image classification using multi-task feature leverage with multi-variant deep learning

Abstract

Deep learning framework aids the researchers in learning different application areas to a greater extent. Deep learning framework is preferred over machine learning since it helps to learn the input from end to end, whereas latter one require the inputs to be cut into pieces according to the need. This paper proposes a Multi-Variant Deep Learning framework for learning and classifying the hyperspectral images. Multi-task Feature Leverage is incorporated by doing two-ordered feature extraction. The first order feature extraction was done by using Two Dimensional Empirical Wavelet Transforms (2D-EWT) and the second-order feature extraction was done by using Stacked Autoencoder (SAE) and Convolutional Neural Network (CNN) for the approximation image of 2D-EWT. Because of the possibility of working on prominent feature, the proposed work uses approximation image than the raw image. The classification was carried out by using Random Forest (RF), Multi- Support Vector Machine (MSVM) and Extreme learning machine (ELM).